The advent of RNA interference has opened new horizons in molecular biology by enabling specific suppression of the function of virtually any gene. RNAi is a sequence-specific posttranscriptional gene silencing mechanism triggered by double-stranded RNA (dsRNA). It causes degradation or translational repression of mRNAs complementary in sequence to the dsRNA. Effective inhibition by the RNAi pathway requires the identification of functional small interfering RNAs (siRNAs) or short hairpin RNAs (shRNAs). To this end, prediction algorithms based on various design rules have been implemented and, recently, improved by the use of artificial neural networks. Nevertheless, these algorithms often fail to correctly forecast si- or shRNA potency.
A number of shRNA libraries have been constructed to date (see, e.g., Bernards et al., (2006), Nature Methods 3, 701-706; Chang et al. (2006), Nature Methods 3, 707-714). But one of the most challenging problems for creating an interfering RNA molecule library is the identification of effective and specific interfering RNA molecules. Potent interfering RNA molecules are needed because partial knockdown does not lead to clear loss-of-function effects. Experimental evidence has shown that differences as subtle as a one base pair (bp) shift on the target mRNA can turn a potent interfering RNA molecule into a weak one.
Design rules have been established by researchers for the creation of effective and specific si- or shRNAs (reviewed in Pei and Tuschl (2006), Nat. Methods 3, 670-676). The most important features include the thermodynamic asymmetry of the RNA duplex, sequence homology of the seed sequence to its cognate target mRNA but not to other mRNAs, and a set of single nucleotide positional preferences. These and further understandings of the RNAi mechanism have been integrated into computer algorithms for in silico prediction of effective and specific shRNAs. Although these programs have improved the design of duplex RNAs, they are nonetheless imperfect. Not every predicted interfering RNA meets the desired thresholds of potency and specificity, so that experimental proof of target protein knockdown remains indispensable. In fact, as part of the findings of this disclosure, it was determined that existing libraries created by such prediction algorithms showed that about 80% of these shRNAs fail to confer efficient target knockdown.
In an effort to improve the design of potent RNAi triggers, various in-silico algorithms and computational tools have been established over the last years. Birmingham et al. ((2007), Nature Protocols 2: 2068-2078) provide a comprehensive overview of existing RNAi design algorithms. For example, BIOPREDsi was developed based on an empirically trained neural network (Huesken et al. (2005), Nat. Biotechnol. 23, 995-1001) and considerably improved the rate of correct predictions. Nevertheless, predictions derived from these algorithms are still not perfect, but typically contain a mix of functional and non-functional RNAi triggers. Therefore, the identification of functional and potent RNAi triggers still requires individual experimental evaluation of each predicted RNAi trigger prior to use in downstream applications. To improve this limitation, it is desirable to develop new experimental approaches to identify effective RNAi triggers, which could complement or even replace rule-driven selection strategies.
Current experimental validation tactics include Western blots, quantitative reverse-transcription polymerase chain reactions (qRT-PCR), mass spectroscopy, and reporter assays. Western blots are advantageous in that they directly measure protein content and are, therefore, one of the most reliable methods, since they report shRNA effects on transcriptional and translational levels. But specific antibodies are not always available, and can be laborious or impossible to produce. In addition, the tagging of a specific gene with e.g., Flag-tags or His-tags, requires intensive cloning steps and is not applicable to endogenous genes. While qRT-PCR may be broadly applicable and can be relatively easy to perform, the downside of this technology is that no precise quantitative readouts can be obtained because only transcriptional effects are reported. Furthermore, both Western blots and qRT-PCR are gene-specific assays. Thus, only parallel but no high-throughput approaches are currently feasible. To a certain degree, mass spectroscopy allows for high-throughput methods and also directly determines protein levels. However, quantification is difficult, especially for non-abundant proteins that often require purification procedures in addition to knowledge about the specific peptide patterns.
The majority of published shRNA reporter assays employ plasmids carrying mRNA target sequence/reporter gene fusions that are co-introduced into cells with the target-specific sh- or si-RNA (reviewed in Pei and Tuschl, 2006, supra; Smart et al. (2005) Biol. Proced. Online 7: 1-7). Such reporter assays may report shRNA activity on the transcriptional and translational levels, but are unsuited for high-throughput methods. Instead, these assays, which often require extensive cloning, are aimed at testing the effect of different sh- or siRNAs directed against a single target mRNA.
Thus, there is a need in the art for a high-throughput in vitro method for rapidly and simultaneously testing, identifying, and ranking interfering RNA molecules that target different sequences. In addition, there is a need for a method to identify target sequences for RNA interference, which can then be used to inform RNAi design for therapeutic applications in human and veterinary medicine.